A human reading some text inferring that a hypothesis is most likely true is textual entailment. It's different from logical consequence in that it's just a hypothesis. If an anon was working on a robowaifu with big tiddies, you might hypothesize he's a tiddie man. Robowaifus need this to gain insight from text and process it to summarize information and answer questions. Typically chatbots emulate this by predicting things from the semantics they've been trained on but this is not true textual entailment. People have the ability to imagine and hypothesize things they've never seen or even thought about before. Progress in curious AI that can imagine possibilities will help with this.
This is the meaningful relationships between concepts. Steering wheel and car are closer together physically than cat and car, but cat and car are much more similar in spelling. Robowaifus need this for understanding context, metaphors and euphemisms. Usually this is implemented by creating embeddings for words, giving each a vector of continuous values. Each dimension in the vector separates words by their most gross common differences first and moves towards learning the more subtle and uncommon nuances. In my opinion this is going to be a dead end though because it isn't really how the brain connects concepts. We can invent completely new concepts with original differences and already know how similar other concepts are to it because our brains our densely connected in intricate interrelated networks where not only the connections are important but also the timing of firings. I expect progress to come in this from applying spiking neural networks to natural language processing.
Is the ability to read text and integrate it with what you already know to grasp its meaning. It requires being able to know the meaning of the words and understand all the relations between them. If you read a book when you're young and enjoy it one way then read it when you're older and enjoy it on a much deeper level, that's increased reading comprehension. This is important for robowaifus to grasp deeper meanings, such as for a research assistant reading difficult texts to gain insights. Most chatbots have no reading comprehension. They're just making statistical predictions instead of processing and reasoning about what they're reading. I feel this could be improved in the short-term by giving algorithms some agency over the text it chooses to read and time to process and lower its uncertainty before outputting a prediction. Unfortunately most NLP approaches are trained in a way that makes them extremely fragile to small changes and they aren't capable of doing online learning to quickly absorb information in one shot. Online learning in NLP hasn't received much research attention yet because large-scale differentiable memory hasn't been feasible until recently, so there should be some exciting progress in this coming in the next few years.
Similar to textual entailment. It's based on common experience. If you're holding an object and let go of it, it's common sense that it's going to fall. Robowaifus need this to make predictions about the world from their experiences. A robowaifu playing and learning about the world needs to be able to intuit that letting go of a grasped object causes it to fall. Very little AI research has gone into this but a major breakthough was made with hindsight experience replay that can continuously learn from all its experiences.
This is being able to grasp the emotion of text and understand if it's positive, neutral or negative, or if it's angry, sad, ironic, happy, excited, etc. Troll farms use this to find sites and posts speaking against the things they're being paid to defend and to discover tensions within a community to split it apart. Social 'scientists' also use it to study and critique internet communities. With sentiment analysis robowaifus can understand the emotional context of what you're saying and respond appropriately, knowing when to give you hugs and when to tell you you're being a wimp.
Just a fancy term for grammaticality. Robowaifus have to understand the rules of a language to construct grammatically correct sentences for communicating clearly with others. Most sentences people write are completely new but we can make sense of what others are saying because we follow agreed upon rules. Like this if talking started I did. It becomes much more difficult to understand what I'm trying to say. A symbolic approach to this is identifying the parts being said, deconstructing it into a sentence tree and checking that structure is following grammar rules. Most approaches don't even care about this. They just leave it to the language model to figure out what to pay attention to and estimate what should be the next word.